In [3]:
import sys
!conda install --yes --prefix {sys.prefix} -c anaconda scikit-learn
!{sys.executable} -m pip install pandas-profiling==2.3.0

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# All requested packages already installed.

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Building wheels for collected packages: pandas-profiling, htmlmin, confuse
  Building wheel for pandas-profiling (setup.py): started
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Installing collected packages: missingno, htmlmin, pytest-pylint, phik, confuse, pandas-profiling, typed-ast
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In [80]:
import pandas as pd
import pandas_profiling
import numpy as np
from sklearn import preprocessing

ms1 = "one-star-michelin-restaurants.csv"
ms2 = "two-stars-michelin-restaurants.csv"
ms3 = "three-stars-michelin-restaurants.csv"

def normalizeRestaurantCsv(frame):
    #price
    priceMode = str(frame["price"].mode(dropna=True)[0])
    frame["price"] = frame["price"].replace(to_replace=np.nan, value=priceMode)
    return frame
In [81]:
print("1 Michelin Star")
ms1Frame = pd.read_csv(ms1)
ms1Frame = normalizeRestaurantCsv(ms1Frame)
ms1Frame.profile_report()

1 Michelin Star
Out[81]:



















In [82]:



    


print("2 Michelin Stars")
ms2Frame = pd.read_csv(ms2)
ms2Frame = normalizeRestaurantCsv(ms2Frame)
ms2Frame.profile_report()



    















    






2 Michelin Stars










    







                
                









    
Out[82]:



























In [83]:



    


print("3 Michelin Stars")
ms3Frame = pd.read_csv(ms3)
ms3Frame = normalizeRestaurantCsv(ms3Frame)
ms3Frame.profile_report()



    















    






3 Michelin Stars










    







                
                









    
Out[83]:



























In [ ]: